Machine Learning
Machine Learning
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
On handling conflicts between rules with numerical features
Proceedings of the 2006 ACM symposium on Applied computing
Resolving rule conflicts with double induction
Intelligent Data Analysis
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Several rule induction schemes generate hypotheses in the form of unordered rule sets. One important problem that has to be addressed when classifying examples with such hypotheses is how to deal with overlapping rules that predict different classes. Previous approaches to this problem calculate class probabilities based on the union of examples covered by the overlapping rules (as in CN2) or assumes rule independence (using naive Bayes). It is demonstrated that a significant improvement in accuracy can be obtained if class probabilities are calculated based on the intersection of the overlapping rules, or in case of an empty intersection, based on as few intersecting regions as possible.